Intrigued by the project, love the idea of exhaustively exploring significance for arbitrary input.
Think you need to provide a few example inputs and outputs on the github for the program.
Also not sure a project focused on decoding meaning and signals benefits from having AI generated interpretations divorced from the inherently human act of sign interpretation. Can be seen in the md file, such a rigidly enforced structured output has forced it to give some averaged amount of weight to different categories and examples, when many are facets of each-other, or purely just expressions of something mentioned earlier. I can see free-form high-temperature llm outputs fed to another model, which serves only to aggregate their core interpretations, providing more insight than what's within the document currently.
Think you need to provide a few example inputs and outputs on the github for the program.
Also not sure a project focused on decoding meaning and signals benefits from having AI generated interpretations divorced from the inherently human act of sign interpretation. Can be seen in the md file, such a rigidly enforced structured output has forced it to give some averaged amount of weight to different categories and examples, when many are facets of each-other, or purely just expressions of something mentioned earlier. I can see free-form high-temperature llm outputs fed to another model, which serves only to aggregate their core interpretations, providing more insight than what's within the document currently.
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